Objectives: In recent years, artificial intelligence (AI) has become a reliable technology in clinical decision support systems with the solutions it offers in the field of medicine. This finding highlights the effectiveness of monkeypox (Mpox) detection. Early diagnosis, timely intervention, and controlled disease management are the support that the informatics world can offer for Mpox. Methods: A review study was conducted to analyze AI-assisted studies on Mpox for identifying disease clusters, tracking cases, predicting future outbreaks, determining mortality risk, diagnosing and managing diseases, and studying disease trends. The studies reviewed in this review for the detection of Mpox disease are generally shaped around machine learning algorithms, deep learning-based methods, hybrid techniques, and ensemble learning approaches. Results: The advantages and disadvantages of each method introduced in this review are detailed, as well as the choice that should be made for the target goal depending on the parameters of hardware capacity, size of the dataset, training time, computational cost, purpose of the model, and application area of the model. Conclusions: This review provides a comprehensive study for researchers and practitioners with solutions to current and future challenges within the framework of the pros and cons of AI technology for Mpox disease.
Akalın et al. (Thu,) studied this question.